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Implicitly Weighted Methods in Robust Image Analysis
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SYSNO ASEP 0379860 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Implicitly Weighted Methods in Robust Image Analysis Author(s) Kalina, Jan (UIVT-O) RID, SAI, ORCID Source Title Journal of Mathematical Imaging and Vision. - : Springer - ISSN 0924-9907
Roč. 44, č. 3 (2012), s. 449-462Number of pages 14 s. Language eng - English Country US - United States Keywords robustness ; high breakdown point ; outlier detection ; robust correlation analysis ; template matching ; face recognition Subject RIV BB - Applied Statistics, Operational Research R&D Projects 1M06014 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000307772900016 EID SCOPUS 84866051470 DOI 10.1007/s10851-012-0337-z Annotation This paper is devoted to highly robust statistical methods with applications to image analysis. The methods of the paper exploit the idea of implicit weighting, which is inspired by the highly robust least weighted squares regression estimator. We use a correlation coefficient based on implicit weighting of individual pixels as a highly robust similarity measure between two images. The reweighted least weighted squares estimator is considered as an alternative regression estimator with a clear interpretation. We apply implicit weighting to dimension reduction by means of robust principal component analysis. Highly robust methods are exploited in tasks of face localization and face detection in a database of 2D images. In this context we investigate a method for outlier detection and a filter for image denoising based on implicit weighting. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2013
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